Definition, motivation and applications of machine learning. Supervised methods: linear regression, decision trees, support vector machines, and artificial neural networks. Unsupervised methods with clustering algorithms. Learning theory. Performance metrics of machine learning methods.
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In order to qualify for the final exam, students should (i) earn at least 14/28 from the homeworks AND (ii) earn at least 12/25 from the term project.
Students are advised to consult their instructors regarding the use of Generative AI tools and their appropriateness in each course. Responsible use of GenAI is encouraged in accordance with Bilkent University's GenAI Guidelines. https://w3.bilkent.edu.tr/bilkent/generative-artificial-intelligence-genai-guideline/
Introduction to machine learning. Linear regression. (Homework 1) Decision tree learning. (Homework 2) Support vector machines. (Homework 3) KNN. (Homework 4) Neural networks. (Homework 5) Learning theory. (Term project submission 1) MIDTERM EXAM Clustering algorithms I. Clustering algorithms II. (Homework 6) Dimensionality reduction techniques. Performance metrics. (Homework 7) Term project presentations. (Term project submission 2) Review. ECTS - Workload Table: Activities Number Hours Workload Term Project (including preparation and presentation if applicable) 1 40 40 Final exam 1 2 2 Homeworks (including preparation) 7 4 28 Preparation for Final exam 1 20 20 Preparation for Midterm exam 1 16 16 Course hours 14 3 42 Midterm exam 1 2 2 Total Workload: 150 Total Workload / 30: 150 / 30 5 ECTS Credits of the Course: 5